Towards Automated, Adaptive, And Mesh-Free Cfd Modelling For Rotorcraft: Point Cloud Generation

Abstract

This work presents a novel point cloud generation framework, a key development towards a fully automated, adaptive, and mesh-free CFD workflow for complex engineering applications. The novelty of the method is the introduction of the Signed Distance Function (SDF) to guide advancing point layers in the near-body region. Insertion/removal mechanisms of points were also proposed. These ensure high-quality boundary layer resolution in the near-body region, regardless of the complexity and topology of the geometry. For the off-body region, Cartesian points are employed for smooth and adaptive point distributions. Compared to conventional advancing front point generation, the proposed method ensures surface-norm point distributions with consistent layer structures, which are critical for boundary layer resolution. Compared to the strand mesh generation, the current method offers greater flexibility with few restrictions on inter-layer connections. The proposed approach is tested for various 2D and 3D benchmark geometries, along with mesh-free modelling results using the generated point clouds.